Estimating the above-ground biomass in miombo savanna woodlands (Mozambique, East Africa) using L-band synthetic aperture radar data

Remote Sensing

Volumn 5, Issue 4, 2013, Pages 1524-1548

  Carreiras, João M B ^a   Melo, Joana B ^a   Vasconcelos, Maria J ^a  

^a INSTITUTO DE INVESTIGAÇÃO CIENTÍFICA TROPICAL   (Portugal)

Author keywords

Above ground biomass; ALOS PALSAR; Bagging stochastic gradient boosting; Carbon; Miombo savanna woodland; Mozambique

Indexed keywords

ABOVE GROUND BIOMASS; ALOS PALSAR; MIOMBO SAVANNA WOODLAND; MOZAMBIQUE; STOCHASTIC GRADIENT BOOSTING;

ACOUSTIC DEVICES; ADAPTIVE BOOSTING; BACKSCATTERING; BIOMASS; CARBON; ESTIMATION; MEAN SQUARE ERROR; PIXELS;

FORESTRY;

EID: 84880431429     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs5041524     Document Type: Article

References (68)

1. Pan, Y.; Birdsey, R.; Fang, J.; Houghton, R.; Kauppi, P.; Kurz, W.; Phillips, O.; Shvidenko, A.; Lewis, S.; Canadell, J.; et al. A Large and persistent carbon sink in the world's forests. Science 2011, 333, 988-993.
2. Le Quere, C.; Raupach, M.; Canadell, J.; Marland, G.; Bopp, L.; Ciais, P.; Conway, T.; Doney, S.; Feely, R.; Foster, P.; et al. Trends in the sources and sinks of carbon dioxide. Nat. Geosci. 2009, doi:10.1038/ngeo689
3. Houghton, R.; Hall, F.; Goetz, S. Importance of biomass in the global carbon cycle. J. Geophys. Res.-Biogeosci. 2009, 114, doi:10.1029/2009JG000935.
4. Gibbs, H.; Brown, S.; Niles, J.; Foley, J. Monitoring and estimating tropical forest carbon stocks: Making REDD a reality. Environ. Res. Lett. 2007, 2, doi:10.1088/1748-9326/2/4/045023.
5. Campbell, B. Beyond Copenhagen: REDD plus, agriculture, adaptation strategies and poverty. Glob. Environ. Chang.-Human Policy Dimens. 2009, 19, 397-399.
6. Danielsen, F.; Skutsch, M.; Burgess, N.D.; Jensen, P.M.; Andrianandrasana, H.; Karky, B.; Lewis, R.; Lovett, J.C.; Massao, J.; Ngaga, Y.; et al. At the heart of REDD: A role for local people in monitoring forests. Conserv. Lett. 2011, 4, 158-167.
7. Herold, M.; Verchot, L.; Angelsen, A.; Maniatis, D.; Bauch, S. A Step-Wise Framework for Setting REDD+ Forest Reference Emission Levels and Forest Reference Levels; Center for International Forestry Research (CIFOR): Bogor, Indonesia, 2012.
8. Mora, B.; Herold, M.; De Sy., V.; Wijaya, A.; Verchot, L.; Penman, J. Capacity Development in National Forest Monitoring: Experiences and Progress for REDD+; Joint Report by CIFOR and GOFC-GOLD: Bogor, Indonesia, 2012.
9. Chambers, J.; Asner, G.; Morton, D.; Anderson, L.; Saatch, S.; Espirito-Santo, F.; Palace, M.; Souza, C. Regional ecosystem structure and function: Ecological insights from remote sensing of tropical forests. Trends Ecol. Evol. 2007, 22, 414-423.
10. Goetz, S.J.; Baccini, A.; Laporte, N.T.; Johns, T.; Walker, W.; Kellndorfer, J.; Houghton, R.A.; Sun, M. Mapping and monitoring carbon stocks with satellite observations: A comparison of methods. Carbon Balan. Manag. 2009, 4, doi:10.1186/1750-0680-4-2.
11. Lucas, R.; Armston, J.; Fairfax, R.; Fensham, R.; Accad, A.; Carreiras, J.; Kelley, J.; Bunting, P.; Clewley, D.; Bray, S.; et al. An Evaluation of the ALOS PALSAR L-Band Backscatter-Above Ground Biomass Relationship Queensland, Australia: Impacts of Surface Moisture Condition and Vegetation Structure. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2010, 3, 576-593.
12. Saatchi, S.; Harris, N.; Brown, S.; Lefsky, M.; Mitchard, E.; Salas, W.; Zutta, B.; Buermann, W.; Lewis, S.; Hagen, S.; et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl. Acad. Sci. USA 2011, 108, 9899-9904.
13. Carreiras, J.M.B.; Vasconcelos, M.J.; Lucas, R.M. Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa). Remote Sens. Environ. 2012, 121, 426-442.
14. Silva, J.M.N.; Carreiras, J.M.B.; Rosa, I.; Pereira, J.M.C. Greenhouse gas emissions from shifting cultivation in the tropics, including uncertainty and sensitivity analysis. J. Geophys. Res.-Atmos. 2011, 116, doi:10.1029/2011JD016056.
15. Hall, F.; Bergen, K.; Blair, J.; Dubayah, R.; Houghton, R.; Hurtt, G.; Kellndorfer, J.; Lefsky, M.; Ranson, J.; Saatchi, S.; et al. Characterizing 3D vegetation structure from space: Mission requirements. Remote Sens. Environ. 2011, 115, 2753-2775.
16. Saatchi, S.; Halligan, K.; Despain, D.; Crabtree, R. Estimation of forest fuel load from radar remote sensing. IEEE Trans. Geosci. Remote Sens. 2007, 45, 1726-1740.
17. Popescu, S.; Zhao, K.; Neuenschwander, A.; Lin, C. Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level. Remote Sens. Environ. 2011, 115, 2786-2797.
18. Lefsky, M.; Harding, D.; Keller, M.; Cohen, W.; Carabajal, C.; Espirito-Santo, F.; Hunter, M.; de Oliveira, R., Estimates of forest canopy height and aboveground biomass using ICESat. Geophys. Res. Lett. 2005, 32, doi:10.1029/2005GL023971.
19. Neumann, M.; Saatchi, S.; Ulander, L.; Fransson, J. Assessing Performance of L- and P-Band polarimetric interferometric SAR data in estimating boreal forest above-ground biomass. IEEE Trans. Geosci. Remote Sens. 2012, 50, 714-726.
20. Edson, C.; Wing, M. Airborne Light Detection and Ranging (LiDAR) for Individual Tree Stem Location, Height, and Biomass Measurements. Remote Sens. 2011, 3, 2494-2528.
21. Saatchi, S.; Houghton, R.; Alvala, R.; Soares, J.; Yu, Y. Distribution of aboveground live biomass in the Amazon basin. Glob. Chang. Biol. 2007, 13, 816-837.
22. Campbell, B.M.; Center for International Forestry Research. The Miombo in Transition: Woodlands and Welfare in Africa; Center for International Forestry Research: Bogor, Indonesia, 1996.
23. Mitchell, R.; Mitchell, M. Conservation for Future Generations: The Miombo Ecoregion; WWF-Southern Africa Regional Programme Office: Harare, Zimbabwe, 2001.
24. European Parliament; Council of the European Union. Directive 2009/28/EC of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30/EC. Official Journal of the European Union 2009, 140, 16-62.
25. Ryan, C.; Williams, M.; Grace, J. Above- and Belowground Carbon Stocks in a Miombo Woodland Landscape of Mozambique. Biotropica 2011, 43, 423-432.
26. Chidumayo, E.N. Stockholm Environment Institute. Miombo Ecology and Management: An Introduction; IT Publications in association with the Stockholm Environment Institute: London, UK, 1997.
27. Chave, J.; Andalo, C.; Brown, S.; Cairns, M.; Chambers, J.; Eamus, D.; Folster, H.; Fromard, F.; Higuchi, N.; Kira, T.; et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 2005, 145, 87-99.
28. Brown, S.; Gillespie, A.; Lugo, A. Biomass estimation methods for tropical forests with applications to forest inventory data. For. Sci. 1989, 35, 881-902.
29. Williams, M.; Ryan, C.; Rees, R.; Sarnbane, E.; Femando, J.; Grace, J. Carbon sequestration and biodiversity of re-growing miombo woodlands in Mozambique. For. Ecol. Manag. 2008, 254, 145-155.
30. Rosenqvist, A.; Shimada, M.; Ito, N.; Watanabe, M. ALOS PALSAR: A Pathfinder mission for global-scale monitoring of the environment. IEEE Trans. Geosci. Remote Sens. 2007, 45, 3307-3316.
31. Ulaby, F.T.; Dobson, M.C. Handbook of Radar Scattering Statistics for Terrain; Artech House: Norwood, MA, USA, 1989.
32. Meier, E.; Frei, U.; Nüesch, D. Precise Terrain Corrected Geocoded Images. In SAR Geocoding: Data and Systems; Schreier, G., Ed.; Herbert Wichmann Verlag GmbH: Karlsruhe, Germany, 1993; pp 173-186.
33. Oliver, C.; Quegan, S. Understanding Synthetic Aperture Radar Images; SciTech Publishing: Raleigh, NC, USA, 2004.
34. Woodhouse, I.H. Introduction to Microwave Remote Sensing; Taylor Francis: Boca Raton, FL, USA, 2006.
35. Holecz, F.; Meier, E.; Piesbergen, J.; Nüesch, D. Topographic Effects on Radar Cross Section. In Proceedings of the CEOS SAR Calibration Workshop, Noordwijk, The Netherlands, 20-24 September 1993; pp. 23-28.
36. Holecz, F.; Meier, E.; Piesbergen, J.; Nüesch, D.; Moreira, J. In Rigorous Derivation of Backscattering Coefficient. In Proceedings of the CEOS SAR Calibration Workshop, Ann Arbor, MI, USA, 28-30 September 1994; pp. 143-155.
37. Steel, R.G.D.; Torrie, J.H. Principles and Procedures of Statistics: A Biometrical Approach, 2nd ed.; McGraw-Hill: New York, NY, USA, 1980.
38. Hastie, T.; Tibshirani, R.; Friedman, J.H. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed.; Springer: New York, NY, USA, 2009.
39. Moisen, G.; Freeman, E.; Blackard, J.; Frescino, T.; Zimmermann, N.; Edwards, T. Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods. Ecol. Model. 2006, 199, 176-187.
40. Elith, J.; Leathwick, J.; Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 2008, 77, 802-813.
41. Carreiras, J.; Pereira, J.; Campagnolo, M.; Shimabukuro, Y. Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data. Remote Sens. Environ. 2006, 101, 283-298.
42. Friedl, M.; Sulla-Menashe, D.; Tan, B.; Schneider, A.; Ramankutty, N.; Sibley, A.; Huang, X. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 2010, 114, 168-182.
43. Friedman, J. Greedy function approximation: A gradient boosting machine. Ann. Stat. 2001, 29, 1189-1232.
44. Friedman, J. Stochastic gradient boosting. Comput. Stat. Data Anal. 2002, 38, 367-378.
45. Breiman, L.; Friedman, J.H.; Olshen, R.A.; Stone, C.J. Classification and Regression Trees; CRC Press: Boca Raton, FL, USA, 1984.
46. Safavian, S.; Landgrebe, D. A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 1991, 21, 660-674.
47. Hansen, M.; Dubayah, R.; DeFries, R. Classification trees: An alternative to traditional land cover classifiers. Int. J. Remote Sens. 1996, 17, 1075-1081.
48. Friedl, M.; Brodley, C. Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 1997, 61, 399-409.
49. Breiman, L. Bagging predictors. Mach. Learn. 1996, 24, 123-140.
50. Breiman, L. Arcing classifiers. Ann. Stat. 1998, 26, 801-824.
51. Bauer, E.; Kohavi, R. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach. Learn. 1999, 36, 105-139.
52. De'ath, G. Boosted trees for ecological modeling and prediction. Ecology 2007, 88, 243-251.
53. Sankaran, M.; Ratnam, J.; Hanan, N. Woody cover in African savannas: The role of resources, fire and herbivory. Glob. Ecol. Biogeogr. 2008, 17, 236-245.
54. Jalabert, S.; Martin, M.; Renaud, J.; Boulonne, L.; Jolivet, C.; Montanarella, L.; Arrouays, D. Estimating forest soil bulk density using boosted regression modelling. Soil Use Manag. 2010, 26, 516-528.
55. Lawrence, D.; Radel, C.; Tully, K.; Schmook, B.; Schneider, L. Untangling a decline in tropical forest resilience: Constraints on the sustainability of shifting cultivation across the globe. Biotropica 2010, 42, 21-30.
56. Suen, Y.; Melville, P.; Mooney, R.; Gama, J.; Camacho, R.; Brazdil, P.; Jorge, A.; Torgo, L. Combining Bias and Variance Reduction Techniques for Regression Trees. In Proceedings of the 2005 European Conference on Machine Learning, Porto, Portugal, 3-7 October 2005; Volume 3720, pp. 741-749.
57. R Development Core Team. R: A Language and Environment for Statistical Computing; 2.14.1; R Foundation for Statistical Computing: Vienna, Austria, 2011.
58. Ridgeway, G. Generalized Boosted Models: A Guide to the GBM package. Available online: http://cran.open-source-solution.org/web/packages/gbm/vignettes/gbm.pdf (accessed on 1 March 2012).
59. Intergovernmental Panel on Climate Change. 2006 IPCC Guidelines for National Greenhouse Gas Inventories; Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., Eds.; Institute for Global Environmental Strategies: Kanagawa, Japan, 2006. Available online: http://www.ipcc-nggip.iges.or.jp/public/2006gl/index.html (accessed on 1 March 2012).
60. Baccini, A.; Laporte, N.; Goetz, S.; Sun, M.; Dong, H. A first map of tropical Africa's above-ground biomass derived from satellite imagery. Environ. Res. Lett. 2008, 3, doi:10.1088/ 1748-9326/3/4/045011.
61. Bevington, P.R.; Robinson, D.K. Data Reduction and Error Analysis for the Physical Sciences, 3rd ed.; McGraw-Hill: Boston, MA, USA, 2003.
62. Richards, J.A. Remote Sensing with Imaging Radar; Springer-Verlag: Berlin, Germany, 2009.
63. Mitchard, E.; Saatchi, S.; Lewis, S.; Feldpausch, T.; Gerard, F.; Woodhouse, I.; Meir, P. Comment on "A first map of tropical Africa's above-ground biomass derived from satellite imagery". Environ. Res. Lett. 2011, 6, doi:10.1088/1748-9326/6/4/049001.
64. Glenday, J. Carbon storage and emissions offset potential in an African dry forest, the Arabuko-Sokoke Forest, Kenya. Environ. Monit. Assess. 2008, 142, 85-95.
65. Malimbwi, R.E.; Solberg, B.; Luoga, E. Estimation of biomass and volume in miombo woodland at Kitulangalo Forest Reserve, Tanzania. J. Trop. For. Sci. 1994, 7, 230-242.
66. Shirima, D.; Munishi, P.; Lewis, S.; Burgess, N.; Marshall, A.; Balmford, A.; Swetnam, R.; Zahabu, E. Carbon storage, structure and composition of miombo woodlands in Tanzania's Eastern Arc Mountains. Afr. J. Ecol. 2011, 49, 332-342.
67. Romijn, H. Land clearing and greenhouse gas emissions from Jatropha biofuels on African Miombo Woodlands. Energy Policy 2011, 39, 5751-5762.
68. Ryan, C.M.; Hill, T.; Woollen, E.; Ghee, C.; Mitchard, E.; Cassells, G.; Grace, J.; Woodhouse, I.H.; Williams, M. Quantifying small-scale deforestation and forest degradation in African woodlands using radar imagery. Glob. Chang. Biol. 2012, 18, 243-257.